Optimizing web search using social annotations
Proceedings of the 16th international conference on World Wide Web
Can social bookmarking enhance search in the web?
Proceedings of the 7th ACM/IEEE-CS joint conference on Digital libraries
Can social bookmarking improve web search?
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
Determining the informational, navigational, and transactional intent of Web queries
Information Processing and Management: an International Journal
Efficient top-k querying over social-tagging networks
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Towards a model of understanding social search
Proceedings of the 2008 ACM conference on Computer supported cooperative work
Learning to recommend with social trust ensemble
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
The anatomy of a large-scale social search engine
Proceedings of the 19th international conference on World wide web
SNDocRank: document ranking based on social networks
Proceedings of the 19th international conference on World wide web
New-web search with microblog annotations
Proceedings of the 19th international conference on World wide web
Search in social networks with access control
Proceedings of the 2nd International Workshop on Keyword Search on Structured Data
Information retrieval in folksonomies: search and ranking
ESWC'06 Proceedings of the 3rd European conference on The Semantic Web: research and applications
Journal of the American Society for Information Science and Technology
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Social search is a variant of information retrieval where a document or website is considered relevant if individuals from the searcher's social network have interacted with it. Our ranking metric Social Relevance Score (SRS) is based on two factors. First, the engagement intensity quantifies the effort a user has made during an interaction. Second, users can assign a trust score to each person from their social network, which is then refined using social network analysis. We have tested our hypotheses with our search engine www.social-search.com, which extends the existing social bookmarking platform folkd.com. Our search engine integrates information the folkd.com users share through the popular social networks Twitter and Facebook. With permission of 2,385 testers, we have connected to their social graphs to generate a large-scale real-world dataset. Over the course of a two-month field study, 468,889 individuals have generated 24,854,281 website recommendations. We have used those links to enhance their search results while measuring the impact on the search behavior. We have found that social results are available for most queries and usually lead to more satisfying results.